Search Results for author: Niloy Mitra

Found 21 papers, 10 papers with code

Differentiable Surface Triangulation

1 code implementation22 Sep 2021 Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov, Paul Guerrero

Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.

GLASS: Geometric Latent Augmentation for Shape Spaces

1 code implementation6 Aug 2021 Sanjeev Muralikrishnan, Siddhartha Chaudhuri, Noam Aigerman, Vladimir Kim, Matthew Fisher, Niloy Mitra

We investigate the problem of training generative models on a very sparse collection of 3D models.

Labels4Free: Unsupervised Segmentation using StyleGAN

1 code implementation ICCV 2021 Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka

We propose an unsupervised segmentation framework for StyleGAN generated objects.

Walk2Map: Extracting Floor Plans from Indoor Walk Trajectories

no code implementations27 Feb 2021 Claudio Mura, Renato Pajarola, Konrad Schindler, Niloy Mitra

Thanks to recent advances in data-driven inertial odometry, such minimalistic input data can be acquired from the IMU readings of consumer-level smartphones, which allows for an effortless and scalable mapping of real-world indoor spaces.

Image-to-Image Translation

Learning Delaunay Surface Elements for Mesh Reconstruction

1 code implementation CVPR 2021 Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov

We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.

Object Properties Inferring from and Transfer for Human Interaction Motions

no code implementations20 Aug 2020 Qian Zheng, Weikai Wu, Hanting Pan, Niloy Mitra, Daniel Cohen-Or, Hui Huang

In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.

Fine-grained Action Recognition

Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

1 code implementation ECCV 2020 Jiahui Lei, Srinath Sridhar, Paul Guerrero, Minhyuk Sung, Niloy Mitra, Leonidas J. Guibas

We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.

StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flows

2 code implementations6 Aug 2020 Rameen Abdal, Peihao Zhu, Niloy Mitra, Peter Wonka

We evaluate our method using the face and the car latent space of StyleGAN, and demonstrate fine-grained disentangled edits along various attributes on both real photographs and StyleGAN generated images.

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

1 code implementation NeurIPS 2020 Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy Mitra

Our experiments show that using explicit 3D features to represent objects allows BlockGAN to learn disentangled representations both in terms of objects (foreground and background) and their properties (pose and identity).

Representation Learning

StructEdit: Learning Structural Shape Variations

1 code implementation CVPR 2020 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.

StructureNet: Hierarchical Graph Networks for 3D Shape Generation

2 code implementations1 Aug 2019 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.

3D Shape Generation

Escaping Plato's Cave: 3D Shape From Adversarial Rendering

no code implementations ICCV 2019 Philipp Henzler, Niloy Mitra, Tobias Ritschel

We can successfully reconstruct 3D shapes from unstructured 2D images and extensively evaluate PlatonicGAN on a range of synthetic and real data sets achieving consistent improvements over baseline methods.

How does Lipschitz Regularization Influence GAN Training?

no code implementations ECCV 2020 Yipeng Qin, Niloy Mitra, Peter Wonka

In this work, we uncover an even more important effect of Lipschitz regularization by examining its impact on the loss function: It degenerates GAN loss functions to almost linear ones by restricting their domain and interval of attainable gradient values.

Unsupervised Intuitive Physics from Visual Observations

no code implementations14 May 2018 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

While learning models of intuitive physics is an increasingly active area of research, current approaches still fall short of natural intelligences in one important regard: they require external supervision, such as explicit access to physical states, at training and sometimes even at test times.

Facade Segmentation in the Wild

no code implementations9 May 2018 John Femiani, Wamiq Reyaz Para, Niloy Mitra, Peter Wonka

Specifically, we propose a MULTIFACSEGNET architecture to assign multiple labels to each pixel, a SEPARABLE architecture as a low-rank formulation that encourages extraction of rectangular elements, and a COMPATIBILITY network that simultaneously seeks segmentation across facade element types allowing the network to 'see' intermediate output probabilities of the various facade element classes.

Semantic Segmentation

Taking Visual Motion Prediction To New Heightfields

no code implementations22 Dec 2017 Sebastien Ehrhardt, Aron Monszpart, Niloy Mitra, Andrea Vedaldi

In order to be able to leverage the approximation capabilities of artificial intelligence techniques in such physics related contexts, researchers have handcrafted the relevant states, and then used neural networks to learn the state transitions using simulation runs as training data.

motion prediction

SeeThrough: Finding Chairs in Heavily Occluded Indoor Scene Images

no code implementations28 Oct 2017 Moos Hueting, Pradyumna Reddy, Vladimir Kim, Ersin Yumer, Nathan Carr, Niloy Mitra

Discovering 3D arrangements of objects from single indoor images is important given its many applications including interior design, content creation, etc.

Object Detection

Learning to Represent Mechanics via Long-term Extrapolation and Interpolation

no code implementations6 Jun 2017 Sébastien Ehrhardt, Aron Monszpart, Andrea Vedaldi, Niloy Mitra

While the basic laws of Newtonian mechanics are well understood, explaining a physical scenario still requires manually modeling the problem with suitable equations and associated parameters.

ImageSpirit: Verbal Guided Image Parsing

no code implementations16 Oct 2013 Ming-Ming Cheng, Shuai Zheng, Wen-Yan Lin, Jonathan Warrell, Vibhav Vineet, Paul Sturgess, Nigel Crook, Niloy Mitra, Philip Torr

This allows us to formulate the image parsing problem as one of jointly estimating per-pixel object and attribute labels from a set of training images.

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